数字农科院2.0

Hyperspectral inversion of leaf nitrogen content in wheat by integrating CWT-SPA feature optimization and XGBoost-SSA model

文献类型： 外文期刊

作者： Gu, Chen;Liu, Huaiyang;You, Yunhao;Zeng, Qianghao;Zhou, Zhenxiang;Song, Ming;Shi, Yun;Tian, Tong

作者机构：

关键词： Remote sensing;Hyperspectral;Leaf nitrogen content;Machine learning;Wheat;Continuous wavelet transform

期刊名称： SMART AGRICULTURAL TECHNOLOGY

ISSN：

年卷期： 2025 年 13 卷

页码：

收录情况： ESCI（2025版）

摘要： Leaf nitrogen content (LNC) is an essential physiological indicator for assessing the growth status of wheat. However, the accuracy and generalization of remote sensing-based monitoring models are often constrained by spatial and temporal variability. To overcome these limitations, this study proposes a cascaded optimization framework that integrates signal enhancement, feature selection, and intelligent optimization algorithms. First, the raw spectral data were preprocessed using Savitzky-Golay (SG) smoothing and a first-order derivative transformation, followed by a multi-scale continuous wavelet transform (CWT). Then, relevant spectral bands were identified through Pearson correlation analysis, and further dimensionality reduction was performed using the successive projections algorithm (SPA). Finally, two regression models were developed: an Extreme Gradient Boosting (XGBoost) model optimized with the Sparrow Search Algorithm (SSA) and an Extreme Learning Machine (ELM) optimized with the Artificial Hummingbird Algorithm (AHA). The XGBoost-SSA model demonstrated superior predictive performance on the test set, achieving a coefficient of determination (R2) of approximately 0.79, a root mean square error (RMSE) of approximately 0.14 mg/g, and a mean absolute percentage error (MAPE) of approximately 9.31%. On an independent external validation set, the XGBoost-SSA model also showed strong generalization capability, maintaining an R2 of approximately 0.73, an RMSE of approximately 0.14 mg/g, and a MAPE of approximately 9.63%. These findings underscore the value of medium-scale CWT-SPA in spectral feature extraction and highlight the advantages of swarm intelligence algorithms in enhancing regression model performance. Overall, the proposed approach provides a reliable solution for high-precision nitrogen monitoring using hyperspectral remote sensing and supports data-driven applications in smart agriculture.

分类号：

  • 相关文献

[1]Improved random patches and model transfer for deriving leaf mass per area across multispecies from spectral reflectance. Shuaipeng Fei,Shunfu Xiao,Demin Xu,Meiyan Shu,Hong Sun,Puyu Feng,Yonggui Xiao,Yuntao Ma. 2024

[2]Accurate modeling of vertical leaf nitrogen distribution in summer maize using in situ leaf spectroscopy via CWT and PLS-based approaches. Li L.,Geng S.,Lin D.,Su G.,Zhang Y.,Chang L.,Ji Y.,Wang Y.,Wang L.. 2022

[3]Monitoring plant response to phenanthrene using the red edge of canopy hyperspectral reflectance. Zhu, Linhai,Wang, Jianjian,Jiang, Lianhe,Zheng, Yuanrun,Zhu, Linhai,Chen, Zhongxin,Wang, Jianjian,Ding, Jinzhi,Ding, Jinzhi,Yu, Yunjiang,Li, Junsheng,Xiao, Nengwen,Rimmington, Glyn M..

[4]充分发掘保护利用生物多样性;促进农业可持续发展——《生物多样性与小麦改良》简评. 李祥洲. 1997

[5]Classification models for Tobacco Mosaic Virus and Potato Virus Y using hyperspectral and machine learning techniques. Haitao Chen,Yujing Han,Yongchang Liu,Dongyang Liu,Lianqiang Jiang,Kun Huang,Hongtao Wang,Leifeng Guo,Xinwei Wang,Jie Wang,Wenxin Xue. 2023

[6]Multi-random ensemble on Partial Least Squares regression to predict wheat yield and its losses across water and nitrogen stress with hyperspectral remote sensing. Bohan Mao,Qian Cheng,Li Chen,Fuyi Duan,Xiaoxiao Sun,Yafeng Li,Zongpeng Li,Weiguang Zhai,Fan Ding,Hao Li,Zhen Chen. 2024

[7]A hybrid method for water stress evaluation of rice with the radiative transfer model and multidimensional imaging. Yufan Zhang,Xiuliang Jin,Liangsheng Shi,Yu Wang,Han Qiao,Yuanyuan Zha. 2025

[8]Spatial variability of soil salinity in coastal saline-alkali farmlands: A novel approach integrating a stacked model with the reconstructed in-situ hyperspectral feature. Dexi Zhan,Yunting Liu,Weihao Yang,Miao Lu,Yingqiang Song. 2025

[9]Evaluation of Spatial Variability of Soil Nutrients in Saline–Alkali Farmland Using Automatic Machine Learning Model and Hyperspectral Data. Meiyan Xiang,Qianlong Rao,Xiaohang Yang,Xiaoqian Wu,Dexi Zhan,Jin Zhang,Miao Lu,Yingqiang Song. 2025

[10]Comparing Machine Learning Algorithms for Pixel/Object-Based Classifications of Semi-Arid Grassland in Northern China Using Multisource Medium Resolution Imageries. Wu N.,Crusiol L.G.T.,Liu G.,Wuyun D.,Han G.. 2023

[11]Estimation of sugar content in sugar beet root based on UAV multi-sensor data. Wang Q.,Che Y.,Shao K.,Zhu J.,Wang R.,Sui Y.,Guo Y.,Li B.,Meng L.,Ma Y.. 2022

[12]Entropy Weight Ensemble Framework for Yield Prediction of Winter Wheat Under Different Water Stress Treatments Using Unmanned Aerial Vehicle-Based Multispectral and Thermal Data. Shuaipeng Fei,Muhammad Adeel Hassan,Yuntao Ma,Meiyan Shu,Qian Cheng,Zongpeng Li,Zhen Chen,Yonggui Xiao. 2021

[13]Spatial-Temporal Characteristics and Driving Forces of Aboveground Biomass in Desert Steppes of Inner Mongolia, China in the Past 20 Years. Wu, Nitu,Liu, Guixiang,Wuyun, Deji,Yi, Bole,Du, Wala,Han, Guodong. 2023

[14]A Method for Estimating Alfalfa (Medicago sativa L.) Forage Yield Based on Remote Sensing Data. Jingsi Li,Ruifeng Wang,Mengjie Zhang,Xu Wang,Yuchun Yan,Xinbo Sun,Dawei Xu. 2023

[15]Editorial: Remote sensing for field-based crop phenotyping. Jiangang Liu,Zhenjiang Zhou,Bo Li. 2024

[16]Review of GNSS-R Technology for Soil Moisture Inversion. Yang C.,Mao K.,Guo Z.,Shi J.,Bateni S.M.,Yuan Z.. 2024

[17]County-Level Cultivated Land Quality Evaluation Using Multi-Temporal Remote Sensing and Machine Learning Models: From the Perspective of National Standard. Dingding Duan,Xinru Li,Yanghua Liu,Qingyan Meng,Chengming Li,Guotian Lin,Linlin Guo,Peng Guo,Tingting Tang,Huan Su,Weifeng Ma,Shikang Ming,Yadong Yang. 2024

[18]Spatial Mapping of Soil CO2 Flux in the Yellow River Delta Farmland of China Using Multi-Source Optical Remote Sensing Data. Wenqing Yu,Shuo Chen,Weihao Yang,Yingqiang Song,Miao Lu. 2024

[19]Potential erosion and sedimentation based on land use change by using cellular automata-artificial neural network. Aditya Nugraha Putra,Istika Nita,Kurniawan Sigit Wicaksono,Novandi Rizky Prasetya,Michelle Talisia Sugiarto,Fahmi Hidayat,Zainal Alim,Sugik Edy Sartono,Pandham Giri Sasangka,Tiar Ranu Kusuma,Bilawal Abbasi,Alena Gessert,Mohd Hasmadi Ismail,Watit Khokthong. 2025

[20]Predicting the greenhouse crop morphological parameters based on RGB-D Computer Vision. Ziqiu Kang,Bo Zhou,Shulang Fei,Nan Wang. 2025

作者其他论文 更多>>